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Business Performance Forecasting With External Data Analytics Tools

Improved business performance forecasting using external data analytics tools has closed the planning accuracy gap, yet many organizations have not made the transition. It is no secret that many companies today are off on their forecasts. Read any recent news article on the topic, and you’ll likely find that most Fortune 500 executives who missed the mark blamed external factors, such as the stronger dollar, a drop in oil prices, or changes in the housing market.

While companies may understand that a correlation exists between the strength of the dollar and their quarterly sales reports, the truth is that when they initially create sales projections, there is usually no proven internal process to determine precisely which factors may or may not impact upcoming performance.

Call it what you will – a gut feeling, historically based assumptions – but typical business forecasting processes are mainly based on guesswork. In fact, only 12% of executives feel highly confident in their forecasting processes, according to a survey we recently conducted.

In a Gatepoint Research report of 100 executives, directors and managers in businesses with revenue greater than $250 million (65% work in Fortune 1000 companies with revenue over $1.5 billion) about their business forecasting processes. We found that most businesses are stuck playing a perpetual guessing game when it comes to forecasting. Typically, the forecasting process included examining last year’s or last quarter’s data to “guess” the next quarterly forecast.

Top three survey executive conclusions:

Executives know their forecasts are inaccurate but don’t realize that the problem lies in the process.

Astonishingly, nearly half of the survey respondents cited they had missed their business targets due to inadequate or inaccurate forecasting, yet 62% thought their forecasting capabilities were better than average. Time and again businesses are reporting that their forecasts are off, but most executives blame external factors – not the forecasting process itself. Improving business performance forecasting using external data analytics tools has proven to close the planning accuracy gap, yet organizations are still not pivoting with the technology.

Executives have difficulty pinpointing key business drivers.

Executives realize that external factors significantly impact business performance. In fact, two-thirds of respondents admitted that external factors have adversely affected the accuracy of their business forecasts. However, despite 77% of respondents depending upon analysts and data scientists, these same respondents still struggle to understand key indicators that impact their businesses. 44% admitted that they either could not or were unsure of how to determine the key drivers of their companies’ performance.

Accurate, timely forecasting is a significant challenge for businesses.

Timing is critical when it comes to predicting business performance. Merely reporting the past doesn’t help executives plan for the future. Nearly 60% of executives in our survey said that accurate, timely forecasts constitute a significant challenge, suggesting that they don’t have the systems and processes in place to gather and leverage real-time intelligence in their forecasting processes.

What’s next in business performance forecasting

Without question, businesses are struggling to create the right processes that will help them build more meaningful forecasts.

“While most companies know their current forecasts continue to be inaccurate,” said Dr. Barry P. Keating is a professor of economics and business analytics in the Department of Finance at the University of Notre Dame, “they don’t realize that their processes need work, and if they do, they just don’t know where to begin.”

“It is helpful to know that the strength of the U.S. dollar impacts your sales, but knowing exactly how and when it will affect all levels of your business – production, logistics, demand and even labor – gives executives a 360° outlook that drives better decisions. With business performance forecasting solutions like Prevedere, executives can take the guesswork out of forecasting and gain the kind of actionable intelligence that produces results.”

According to Dr. Keating, many enterprise companies are using outdated data analytics tools and science for forecasting methods. Dr. Keating, as a veteran in the space long before ‘Big Data’, was a frequent phrase, he can see why large companies with an abundance of accessible information are still missing the mark.

“Thirty years ago, the problem companies had was that very little data was available. Companies often didn’t keep track of sales, price changes or promotions for at least not two or three years in the past. Most of the models we used were time-series models that have been around for 50, maybe 100 years, such as moving averages, exponential smoothing or ARIMA,” said Dr. Keating.

The reason time series models have been used for so long, he explained, is because they handle trends, seasonality, and cyclicality very well. However, those models are blind to changes in the economy.

“The federal reserve bank of St. Louis did a study four years ago and found that anytime there is a downturn in the economy, the forecast errors of the Fortune 500 quadruple! The reason is – they are using time-series models. They don’t have that ability to see ahead regarding the economic changes,” Dr. Keating added.

Reflecting on Prevedere’s exogenous and external data analytics tools and science-driven approach to forecasting, Dr. Keating explains how greater forecasting accuracy offers value across an organization “Notre Dame uses Prevedere in the college of business for students interested in going into analytics. That’s virtually every student, whether it is marketing, management, accounting, finance, every area uses analytics,” said Dr. Keating.

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